| Challenge: | EmpathicStories is a dataset of 1,500 personal stories annotated with empathic similarity features and 2,000 pairs of stories annnotated by empathism. |
| Approach: | They propose a task to identify similarity in personal stories based on empathic resonance . they use a dataset of 1,500 personal stories annotated with empathism features . |
| Outcome: | The proposed model outperforms semantic similarity models on correlation and retrieval metrics. |
Similar Papers
EmpathicStories++: A Multimodal Dataset for Empathy Towards Personal Experiences (2024.findings-acl)
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Jocelyn Shen, Yubin Kim, Mohit Hulse, Wazeer Zulfikar, Sharifa Alghowinem, Cynthia Breazeal, Hae Park
| Challenge: | Existing datasets for empathy modeling are limited in the ways they are not captured in the wild. |
| Approach: | They propose a multimodal dataset for empathy during personal experience sharing that contains 53 hours of video, audio, and text data of 41 participants. |
| Outcome: | The EmpathicStories++ dataset contains 53 hours of video, audio, and text data of 41 participants sharing vulnerable experiences and reading empathically resonant stories with an AI agent. |
HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs (2024.emnlp-main)
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| Challenge: | Empathy is a foundational psychological process that drives many prosocial functions. |
| Approach: | They propose a theory-based taxonomy that delineates elements of narrative style that can lead to empathy with the narrator of a story. |
| Outcome: | The proposed taxonomy delineates elements of narrative style that can lead to empathy with the narrator of a story. |
Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs (2024.findings-emnlp)
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| Challenge: | Empathy plays a pivotal role in fostering prosocial behavior, often triggered by the sharing of personal experiences through narratives. |
| Approach: | They propose to use contrastive learning with masked LMs and supervised fine-tuning with large language models to improve empathy understanding in NLP models. |
| Outcome: | The proposed methods show that there is low agreement among annotators and that cultural differences are a factor in their interpretation of empathy. |
Story Embeddings — Narrative-Focused Representations of Fictional Stories (2024.emnlp-main)
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| Challenge: | Existing approaches to model fictional narratives have focused on the aspect of "what" rather than "how" they are being told. |
| Approach: | They propose a model that embeds stories such that similar stories will result in similar embeddings. |
| Outcome: | The proposed model shows state-of-the-art performance on multiple retrieval tasks and a narrative understanding task. |
REG: Retrieval via Emotion Similarity for Guiding Empathetic Dialogue Generation (2026.acl-long)
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| Challenge: | Empathy relies on the cognitive capacity to relate to similar past experiences. Existing methods prioritize semantic similarity over emotion characteristics, leading to unempathetic responses. |
| Approach: | They propose a framework that integrates four Emotion Attributes into the retrieval process to ensure explicit emotional alignment. |
| Outcome: | Empirical results show that REG significantly outperforms baselines, offering a robust solution for empathetic generation. |
Modeling Empathy and Distress in Reaction to News Stories (D18-1)
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| Challenge: | a recent work on empathy prediction has underestimated the complexity of the phenomenon and lacks a shared corpus. authors present a novel annotation methodology which reliably captures empathy assessments by the writer of a statement using multi-item scales. |
| Approach: | They propose a method which captures empathy assessments by the writer of a statement using multi-item scales. |
| Outcome: | The proposed method distinguishes between multiple forms of empathy, empathic concern, and personal distress, as recognized throughout psychology. |
EmpHi: Generating Empathetic Responses with Human-like Intents (2022.naacl-main)
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| Challenge: | Existing empathetic dialogue models lack emotion-dependent response generation . elaine mccartney: "i'm sorry to hear that! " |
| Approach: | They propose a model to generate empathetic responses with human-consistent intents . they aim to address the bias of the empathic intent distribution between epd models and humans . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of empathy, relevance, and diversity on automatic and human evaluation. |
A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support (2020.emnlp-main)
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| Challenge: | Empathy measurement has predominantly occurred in synchronous, face-to-face settings, and may not translate to asynchronous, text-based contexts. |
| Approach: | They propose a computational approach to understanding how empathy is expressed in online mental health platforms. |
| Outcome: | The proposed model can identify empathic conversations and extract rationales from them. |
Modeling Empathetic Alignment in Conversation (2024.naacl-long)
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| Challenge: | Empathy requires perspective-taking and is not explicitly modelled in NLP . |
| Approach: | They propose a new approach to recognizing alignment in empathetic speech, grounded in Appraisal Theory, and use reddit to study emotional conversations to examine alignment. |
| Outcome: | The proposed approach can recognize appraisals and alignments in empathetic speech, and mental health professionals engage with substantially more emotional alignment. |
Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset (P19-1)
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| Challenge: | EmpatheticDialogues dataset provides a benchmark for empathetic dialogue generation . human evaluators perceive dialogue models as more epathetic . |
| Approach: | They propose a benchmark for empathetic dialogue generation from a dataset of 25k conversations grounded in emotional situations. |
| Outcome: | The proposed benchmarks show that existing models are perceived to be more empathetic by human evaluators compared to models trained on large-scale Internet conversations. |